Novel approach to nonlinear/non-Gaussian Bayesian state estimation

TLDR

The authors propose the bootstrap filter as a recursive Bayesian filtering algorithm. The bootstrap filter represents the state density with random samples that are updated and propagated, enabling application to any state transition or measurement model and improving efficiency in a bearings‑only tracking simulation. In the bearings‑only tracking example, the bootstrap filter markedly outperforms the standard extended Kalman filter.

Abstract

An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.